Iraq and the Number of US Casualties

To begin this project I took a CIA jpeg image of Iraq from 2003 from the University of Texas Map Library website and used ArcMap to georeference the image in the Lambert Conformal Conic projection. I then digitized the map by “drawing” over the georeferenced jpeg image and thus made my own map of Iraq. To then make a thematic map of the casualties that the United States has thus far sustained in the war with Iraq I went to the icasualties.org website and found a map with this information. Then to the provinces layer’s attributes table I joined an excel spreadsheet data table which I made from the information I found.
From the resulting map that I made of US casualties in Iraq, the provinces of Baghdad and Al Anbar are by far the most dangerous provinces in the entire country. This would make sense since Baghdad is the capital city of Iraq, and thus its province has seen the most combat during the United States invasion of the country. Also the city of Fallujah in Al Anbar has been a hotspot for conflict between US troops and Iraqis which is demonstrated in the high number of casualties.
In the northern provinces of Iraq there are far less American casualties which is probably due to the fact that the Kurds claim this region as Kurdistan, which the United States supports. Therefore it would be reasonable to assume that less fighting in this region occurs between Iraqis and American forces since the Kurds who make up the majority of Iraqis who live here are not in conflict with the United States. The central provinces of Iraq, which include Al Anbar, Salah Ad Din, Diyala, Baghdad, and Babil, fall within the United States’ occupation zone, which is why there would be more US casualties in those regions.
The provinces of Karbala, Wasit, Al Qadisyah and An Najaf are occupied by Polish-led multinational forces while the southern most provinces of Al Muthanna, Dhi Qar, Maysan and Al Basrah are occupied by British-led multinational forces. Consequently since the United States has fewer troops stationed in these provinces the map reflects the obvious conclusion that there are less American casualties in these regions. Al Basrah, however, as the southern-most province of Iraq, is also where the Euphrates and Tigris rivers meet at the Persian Gulf, and thus is a key point of entry for troops into Iraq. Thus Al Basrah has a slightly higher casualty rate than the other southern provinces which could be due to the fact that is was one of the entry points for invasion by US troops from vessels stationed in the Gulf.

Iraq

Willow Fire Analysis

In 1999 there was a fire in San Bernardino County which burned various vegetation types. This lab’s goal is to determine the total burn area, which types of vegetation were affected and to what degree. To answer these questions it required that I upload data which showed representative polygons of the burn areas over a six day time span. These various polygons sometimes overlapped at certain points, indicating that these areas continued to burn between the time periods in which the data was taken. To remove these overlaps, I combined these six polygons into one overall burn area boundary feature. Next, I merged the data containing California’s vegetation types to fall within the boundary of the burn area. I used ArcToolbox to intersect these two layers—California’s vegetation types and the burn area boundary—and thus created a new layer which showed the different vegetation types that burned in the fire. By opening the attribute table for this layer it showed ten different vegetation records, however, there are only six vegetation types. These vegetation types are Coastal Scrub, Desert Scrub, Juniper, Montane Harwood-Conifer, Pinyon-Juniper, and Urban-Agriculture. To merge the duplicate vegetation types, I used the Dissolve tool and thus concluded with an attribute table that showed the six vegetation types and the total area for each type that burned. Since the map projection being used in this project is the Universal Transverse Mercator Zone 11, measurements are all taken in meters. Our results, however, are supposed to be shown in acres, I found a website with a unit converter and converted all of the areas originally measured in square meters into acres.

In my analysis of the Willow Fire burn area in San Bernardino County, we can see that the total area of the burn site was 68,725 acres and consisted of various types of vegetation being affected by the fire. For the Urban-Agriculture vegetation type 2,052 acres were burned, which consisted of the smallest percentage of the total burn area, only about 3 percent. More significantly 21,974 acres of Pinyon-Juniper and 17704.8 acres of Desert Scrub burned in the fire, accounting for about 31 percent and 25 percent of the total burn area. It would make sense that Urban Agriculture would account for the smallest percentage of the burn site, since agriculture sites generally are well irrigated and thus could be protected better from fire damage. Desert scrub on the other hand is very dry and thus susceptible to fire. The other types of vegetation which burned in the fire were 9,655 acres of Coastal scrub accounting for 14.1% of the total burn area, 9,660 acres of Juniper which was also about 14.1% of the burn, and 7,678.3 acres of Montane Hardwood-Conifer which made up the remaining 11.2% of the fire. Ultimately we can see from the map and the analysis that it provides that there was a significant amount of damage caused by this fire. Since this area of San Bernardino County contains several vegetation types that are more flammable, such as Desert scrub and Pinyon-Juniper vegetation, fire in this area was able to spread quickly and burn tens of thousands of acres.

Census 2000

In this project I took county-level race data from the 2000 Census and used it to create maps showing the population density of various races across the continental United States. I made three individual maps displaying Black, Asian, and Some Other Race categories. To begin this project I started with the map I created in the ArcGIS Census2000 tutorial which included an excel table with basic census data such as county area, population per square mile, etc. To this table I joined the race data that I collected from the US Census Bureau website. To do this, however, required me to clean up the census data I collected and simplify the headings in of the excel spreadsheet. By using the same State and County FIPS (codes for identify the individual counties) in each of the census data tables, I was able to use this specific field to create the join. Thus in doing so I was able to add this new race census data and create my three maps of Black, Asian, and Some Other Race population densities.
When choosing a map projection I decided to use the Lambert conic projection for all three maps because I feel that this projection shows a relatively accurate model of the continental United States. Next when it came time to classify the race data, I chose to use the natural breaks to model the data. I feel that using natural breaks instead of a preselected range of percentages yields a more accurate representation of the data that I am displaying. I also chose to use only four categories and grayscale for simplicity.
When looking at the map of black race data it shows that in the year 2000 a higher percentage of blacks lived in counties found in the Southeastern states such as Louisiana, Mississippi, Alabama, Georgia, South Carolina, North Carolina and Virginia. Also near Chicago, Illinois there is a relatively large black population. The map of Asian race census data shows that the highest population densities of Asians are found on the West Coast, especially around the San Francisco Bay Area. One trend that I can see from looking at both the Black and Asian race census data is that the highest populations of each race in America are found nearer to their original homelands. More Asians live on the West Coast which is closer to Asia and more Blacks live in the Southeastern United States which is closer to Africa. It is interesting that in the year 2000 immigration to America--either recent or centuries ago, willingly or forced--people for the most part people have remained closest to their native homeland.
The map for Some Other Race shows highest population densities from Texas westward through the Sunbelt up to Washington. According to the US Census Bureau website, “some other race” includes all other responses not included in “White,” “Black,” “American Indian and Alaska Native,” “Asian,” and “Native Hawaiian and Other Pacific Islander” race categories. Thus this race category is very broad and can include people who are multiracial, interracial, or consider themselves to be from a particular Hispanic/Latino group--Mexican, Puerto Rican, or Cuban--or those who are Jewish. Therefore since this racial category is not very specific, it is difficult to determine and patterns or trends from the map. The most one could infer from this map is that the majority of people who consider themselves in this category are found in the Center-West part of the continental United States and that most people on the East Coast do not consider themselves in this category.

Projections 101

Map Projections 101 Report

This lab demonstrates the significance of map projections and how different projections can show you very different representations of the world. Since we are trying to make an accurate two dimensional model of the Earth, which is three dimensional, selecting the correct type of map projection requires consideration of what is the purpose of the map. Since every projection causes some distortion, some projections more so than others, making the correct choice of map projection should be determined by what type of information is hoped to be derived from the map.

In our case for this lab, we wanted to figure out the distance between Washington D.C. and Baghdad, Iraq. When each of the six different map projections were applied to our original map, it yielded very different results when determining the distance between these two cities. The conformal projections had drastically different measurements, with the Mercator projection stating 8,414.87 miles from Washington D.C. to Baghdad and the Gall Stereographic projection stating 5,951.85. The equal area Bonne and Sinusoidal projections showed 6,103.85 miles and 6,732.92 miles between the cities while the equidistant projections Plate Carree and Equidistant Conic measured the distance to be 8,410.27 miles and 6,266.63 miles. I went online and looked up the distance between Washington D.C. to Baghdad on www.timeanddate.com and it said that the shortest distance between these cities is 6213 miles. If this data is correct, it suggests that the Equidistant Conic and Bonne map projections are the most accurate.

When looking at these six different projections we can see the various degrees of distortion. The conformal projections seem to maintain more accuracy in displaying the land found closer to the equator, however towards the poles there is significant distortion--Greenland is much too large and Antarctica is humungous. Between the two conformal projections, the Gall Stereographic projection is a better map projection than the Mercator, especially for our purpose in determining the distance between Washington D.C. and Baghdad. Of the equal area projections, the Bonne projection significantly distorted some landmasses such as Australia which is shown to be much larger than it is. Also land towards the North Pole, specifically Greenland, Russia, and Canada, are proportionally much smaller to the than they are in actuality. Nonetheless, land nearer the equator seems to be more accurately preserved and thus the Bonne projection provided us with the most accurate distance between our selected cities. Finally the equidistant projections provided us some of the best and worst measured distances between Washington D.C. and Baghdad. The Plate Carree projection looks strangely warped with the southern hemisphere larger than normal while the northern hemisphere is shrunk, which probably contributes to the great exaggeration of the distance between our selected cities. The Equidistant Conic projection, like the Mercator, shows Antarctica to be too great in size, yet nonetheless provides us with a relatively accurate distance between Washington D.C. and Baghdad.

Ultimately choosing the correct map projection to use requires one to determine which projection will display the best results in answering the given question. Since map projections can distort shape, area, distance, and direction, it is important to identify the projection that will provide the least amount of distortion to properly display a map suitable to answer the given question. Thus in our case, since our question was in regards to distance, choosing a map projection that distorted distance the least was most important in answering our question. Though having a map that does not distort everything else too much is still important, preserving distance was first to shape, area, and direction.